基于三维柔性多体仿真与深度神经网络实现液压驱动系统实时结构挠度估计
Real-Time Structural Deflection Estimation in Hydraulically Actuated Systems Using 3D Flexible Multibody Simulation and DNNs
AI总结：
本文提出基于SLIDE的实时结构挠度估计框架，结合随机数据采集算法和深度神经网络，在液压驱动柔性臂架上实现比多体仿真快10^7倍的挠度估计，为实时控制与监测提供支持。
摘要：
重型机械中轻质高强钢结构的精度、稳定性和性能受到其高度非线性动力学的影响。这反过来又使控制更加困难，仿真计算量更大，并且使用标准方法实现实时自主性变得不可能。然而，通过数据驱动、物理信息和物理启发网络进行机器学习，有望为非线性动力学问题提供计算效率更高且更准确的解决方案。本研究提出了一种新颖的框架，用于估计液压驱动三维系统中的实时结构挠度。该框架基于SLIDE，这是一种基于机器学习的方法，用于估计受迫激励下机械系统的动态响应。此外，还引入了一种算法，用于利用随机初始构型和液压压力从液压驱动系统中采集数据。该新框架在一个液压驱动的柔性臂架上进行了测试，使用了多种传感器组合并提升不同负载。神经网络使用PyTorch中的标准参数、ADAM优化器、多种传感器输入以及最少的输出数据，在更短的时间内成功完成训练。经SLIDE训练的神经网络将挠度估计求解速度相对于柔性多体仿真批次提高了$10^7$倍，并提供了合理的精度。这些结果支持了本研究的目标，即为控制、机器人机械臂、结构健康监测和自动化问题提供鲁棒的实时解决方案。
英文摘要：
The precision, stability, and performance of lightweight high-strength steel structures in heavy machinery is affected by their highly nonlinear dynamics. This, in turn, makes control more difficult, simulation more computationally intensive, and achieving real-time autonomy, using standard approaches, impossible. Machine learning through data-driven, physics-informed and physics-inspired networks, however, promises more computationally efficient and accurate solutions to nonlinear dynamic problems. This study proposes a novel framework that has been developed to estimate real-time structural deflection in hydraulically actuated three-dimensional systems. It is based on SLIDE, a machine-learning-based method to estimate dynamic responses of mechanical systems subjected to forced excitations.~Further, an algorithm is introduced for the data acquisition from a hydraulically actuated system using randomized initial configurations and hydraulic pressures.~The new framework was tested on a hydraulically actuated flexible boom with various sensor combinations and lifting various payloads. The neural network was successfully trained in less time using standard parameters from PyTorch, ADAM optimizer, the various sensor inputs, and minimal output data. The SLIDE-trained neural network accelerated deflection estimation solutions by a factor of $10^7$ in reference to flexible multibody simulation batches and provided reasonable accuracy. These results support the studies goal of providing robust, real-time solutions for control, robotic manipulators, structural health monitoring, and automation problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
